Modern consumers are inundated with choices. Electronic retailers and content providers often offer a substantial selection of products to meet a variety of special needs and tastes. Matching consumers with most appropriate products or services is not trivial, yet it is a key in enhancing user satisfaction and loyalty. This emphasizes the prominence of recommender systems, which provide personalized recommendations for products and services that suit a user's taste. Recommender systems generally use either of two strategies: content-based recommendations or collaborative filtering. The content-based approach profiles each user or product to associate users with matching products. For example, a movie profile might describe its genre, the participating actors, its box office popularity, etc. User profiles could include demographic information or answers to a suitable questionnaire. As such, content-based strategies require gathering external information that might not be available or easy to collect. In contrast, collaborative filtering (CF) relies on past user behavior, e.g., their previous transactions or product or service ratings, and does not require the creation of explicit profiles. Notably, collaborative techniques require no domain knowledge and avoid the need for extensive data collection. In addition, relying directly on user behavior facilitates uncovering complex and unexpected patterns that would be difficult or impossible to profile using content-based recommendation techniques.
In order to establish recommendations, conventional collaborative filtering recommender systems compare fundamentally different objects: items against users. There are two primary approaches to facilitate this comparison: neighborhood modeling and latent factor modeling. Conventional neighborhood models center on determining the relationships between items or, alternatively, between users. An item-oriented neighborhood model approach evaluates the preference of a user to an item based on ratings of similar items by the same user. In a sense, this approach transforms users to the item space by viewing them as baskets of rated items. This way, it is not necessary to directly compare users to items, but rather items can be directly related to other items.
Latent factor models, such as those based on Singular Value Decomposition (SVD), comprise an alternative approach by transforming both items and users to the same latent factor space, thus making them directly comparable. The latent factor space attempts to explain ratings by characterizing both items and users on factors automatically inferred from user feedback. For example, when the items are movies, factors might measure obvious dimensions such as comedy vs. drama, amount of action, or orientation to children; as well as less well-defined dimensions such as depth of character development or quirkiness, or completely uninterpretable dimensions. Neighborhood models are most effective at detecting very localized relationships. They rely on a few significant neighborhood relations, often ignoring the vast majority of ratings by a user.
The use of the same reference symbols in different drawings indicates similar or identical items.